start_date <- "2017-01-01"
end_date <- "2019-12-31"
f1<-function(d2, d1){
n_weeks <- floor(as.numeric(difftime(d2, d1, units="weeks")))
}
f2<-function(d2, d1){
n_weeks <- floor(as.numeric(difftime(as.Date(d2)
, as.Date(d1), units = "weeks")))
}
m1<-microbenchmark(
Nocast = f1(end_date, start_date),
Cast = f2(end_date, start_date),
times = 1000
)
print(m1)
## Unit: microseconds
## expr min lq mean median uq max neval
## Nocast 383.075 390.7645 419.8791 397.262 419.187 3225.200 1000
## Cast 127.117 131.2550 141.7413 133.129 142.541 2525.855 1000
fig <- fbox_plot(m1, "microseconds")
fig
create_c <- function (n){
x <- c()
for (i in seq(n)) {
x <- c(x, i)
}
}
create_vector <- function (n){
x <- vector("integer", n)
for (i in seq(n)) {
x[i] <- i
}
}
m3 <- microbenchmark(
with_c = create_c(1e4),
with_vector = create_vector(1e4),
times = 10
)
print(m3)
## Unit: microseconds
## expr min lq mean median uq max neval
## with_c 65955.37 66058.682 69468.1536 66148.52 75155.912 81450.160 10
## with_vector 342.60 345.975 622.1023 353.48 371.143 3024.906 10
fig <- fbox_plot(m3, "microseconds")
fig
vector <- runif(1e8)
w1 <- function(x){
d <- length(which(x > .5))
}
w2 <- function(x){
d <- sum(x > .5)
}
m4 <- microbenchmark(
which = w1(vector),
nowhich = w2(vector),
times = 10
)
print(m4)
## Unit: milliseconds
## expr min lq mean median uq max neval
## which 624.4498 626.7169 644.8899 630.9330 632.9892 708.7845 10
## nowhich 217.9415 219.1952 236.0176 223.0684 224.5313 299.7681 10
fig <- fbox_plot(m4, "miliseconds")
fig
n <- 1e4
dt <- data.table(
a = seq(n), b = runif(n)
)
v1 <- function(dt){
d <- mean(dt[dt$b > .5, ]$a)
}
v2 <- function(dt){
d <- mean(dt$a[dt$b > .5])
}
m5 <- microbenchmark(
row_operation = v1(dt),
column_operation = v2(dt),
times = 10
)
print(m5)
## Unit: microseconds
## expr min lq mean median uq max neval
## row_operation 163.615 168.364 893.0288 176.7500 195.946 5314.840 10
## column_operation 58.429 65.021 268.5103 69.2095 77.315 2056.819 10
fig <- fbox_plot(m5, "microseconds")
fig
The function seq prevents when the second part of the 1:x is zero
num <- 1e7
s1 <- function(num){
d <- mean(1:num)
}
s2 <- function(num){
d <- mean(seq(num))
}
m6<-microbenchmark(
noseq = s1(num),
seq = s2(num),
times = 30
)
print(m6)
## Unit: milliseconds
## expr min lq mean median uq max neval
## noseq 69.83089 69.88638 69.96562 69.91265 69.95498 71.41214 30
## seq 69.84399 69.91782 69.99335 69.95550 69.98267 71.35749 30
fig <- fbox_plot(m6, "miliseconds")
fig
large_dataset <- data.table(
id = 1:1000000,
value = sample(letters, 1000000, replace = TRUE)
)
a1 <- function(x){
d <- x %>% mutate(code = paste0(id, "_", value))
}
a2 <- function(x){
d <- x %>% mutate(code = glue("{id}_{value}"))
}
m7 <- microbenchmark(
with_paste = a1(large_dataset),
with_glue = a2(large_dataset),
times = 20
)
print(m7)
## Unit: milliseconds
## expr min lq mean median uq max neval
## with_paste 552.6167 557.1574 562.2372 560.558 563.8308 594.2514 20
## with_glue 573.0987 577.9873 599.4778 580.664 583.7402 957.0961 20
fig <- fbox_plot(m7, "miliseconds")
fig
# Example data
data <- data.table(group = rep(seq(10), each = 100), value = rnorm(1000))
print(table(data$group))
##
## 1 2 3 4 5 6 7 8 9 10
## 100 100 100 100 100 100 100 100 100 100
# Using a for loop
for_loop_function <- function(data) {
res <- list()
unique_groups <- unique(data$group)
for(this_group in unique_groups) {
res[[this_group]] <- data %>% filter(group == this_group)
}
return(res)
}
sapply_function <- function(data){
unique_groups <- unique(data$group)
res <- list()
sapply(unique_groups, function(this_group){
res[[this_group]] <<- data %>% filter(group == this_group)
})
return(res)
}
m8 <- microbenchmark(
for_loop = for_loop_function(data),
sapply = sapply_function(data),
times = 500
)
print(m8)
## Unit: milliseconds
## expr min lq mean median uq max neval
## for_loop 6.498610 6.629428 7.017537 6.699711 6.775863 51.19699 500
## sapply 6.565846 6.693649 6.941466 6.761019 6.844546 15.76947 500
fig <- fbox_plot(m8, "miliseconds")
fig
## Unit: microseconds
## expr min lq mean median uq max neval
## Date 1445.498 1491.2485 1738.5549 1532.510 1863.5890 3916.971 200
## iDate 571.246 591.3335 682.4735 618.344 654.5415 2561.762 200
fig <- fbox_plot(m9, "miliseconds")
fig
switch_function <- function(x) {
switch(x,
"a" = "apple",
"b" = "banana",
"c" = "cherry",
"default")
}
case_when_function <- function(x) {
case_when(
x == "a" ~ "apple",
x == "b" ~ "banana",
x == "c" ~ "cherry",
TRUE ~ "default"
)
}
# Create a vector of test values
test_values <- sample(c("a", "b", "c", "d"), 1000, replace = TRUE)
m10 <- microbenchmark(
switch = sapply(test_values, switch_function),
case_when = sapply(test_values, case_when_function),
times = 200L
)
print(m10)
## Unit: microseconds
## expr min lq mean median uq max
## switch 630.266 639.604 664.4921 645.39 657.512 2150.794
## case_when 226570.715 233691.301 235931.5762 235607.96 236796.573 331029.292
## neval
## 200
## 200
fig <- fbox_plot(m10, "microseconds")
fig
set.seed(123)
n <- 1e6
data <- data.table(
id = seq(n),
value = sample(seq(100), n, replace = TRUE)
)
casewhenf <- function(data){
df <- data %>%
mutate(category = case_when(
value <= 20 ~ "Low",
value <= 70 ~ "Medium",
value > 70 ~ "High"))
}
fcasef <- function(data){
df <- data %>%
mutate(category = fcase(
value <= 20, "Low",
value <= 70, "Medium",
value > 70, "High"))
}
m11 <- microbenchmark(
case_when = casewhenf(data),
fcase = fcasef(data),
times = 20
)
print(m11)
## Unit: milliseconds
## expr min lq mean median uq max neval
## case_when 61.27768 61.57658 65.58367 61.70647 69.79883 80.42101 20
## fcase 21.91152 21.99503 22.94816 22.07832 22.39973 28.21466 20
fig <- fbox_plot(m11, "miliseconds")
fig
set.seed(123)
DT <- data.table(
ID = 1:1e6,
Value1 = sample(c(NA, 1:100), 1e6, replace = TRUE),
Value2 = sample(c(NA, 101:200), 1e6, replace = TRUE)
)
# Define the functions
replace_na_f <- function(data){
DF <- data %>%
mutate(Value1 = replace_na(Value1, 0),
Value2 = replace_na(Value2, 0)) %>%
as.data.table()
}
fcoalesce_f <- function(data){
DF <- data %>%
mutate(Value1 = fcoalesce(Value1, 0L),
Value2 = fcoalesce(Value2, 0L))
}
m12 <- microbenchmark(
treplace_na = replace_na_f(DT),
tfcoalesce = fcoalesce_f(DT),
times = 20
)
print(m12)
## Unit: milliseconds
## expr min lq mean median uq max neval
## treplace_na 7.431031 7.547438 8.065811 7.763336 8.346253 10.204548 20
## tfcoalesce 1.530096 1.609288 2.064029 1.914384 2.290365 3.994967 20
fig <- fbox_plot(m12, "miliseconds")
fig
dt <- data.table(field_name = c("argentina.blue.man.watch",
"brazil.red.woman.shoes",
"canada.green.kid.hat",
"denmark.red.man.shirt"))
# Filter rows where 'field_name' does not contain 'red'
dtnot <- function(data){
filtered_dt <- data |> _[!grepl("red", field_name)]
}
dplyrnot <- function(data){
filtered_dt <- data %>% filter(!grepl("red", field_name))
}
m13 <- microbenchmark(
tdtnot = dtnot(dt),
tdplyrnot = dplyrnot(dt),
times = 100
)
print(m13)
## Unit: microseconds
## expr min lq mean median uq max neval
## tdtnot 101.399 111.2125 148.0785 131.6110 139.0645 1964.697 100
## tdplyrnot 668.067 692.6130 768.1297 706.5535 732.0465 3121.225 100
fig <- fbox_plot(m13, "microseconds")
fig
large_data <- data.table(
id = 1:100000,
var1 = rnorm(100000),
var2 = rnorm(100000),
var3 = rnorm(100000),
var4 = rnorm(100000)
)
# Benchmarking
m14 <- microbenchmark(
tidyr_pivot_longer = {
long_data_tidyr <- pivot_longer(large_data, cols = starts_with("var"),
names_to = "variable", values_to = "value")
},
data_table_melt = {
long_data_dt <- melt(large_data, id.vars = "id", variable.name = "variable",
value.name = "value")
},
times = 10
)
print(m14)
## Unit: microseconds
## expr min lq mean median uq max
## tidyr_pivot_longer 6280.974 6354.651 7954.2870 6394.480 6553.002 21716.064
## data_table_melt 463.776 501.196 574.8751 528.767 635.767 796.366
## neval
## 10
## 10
fig <- fbox_plot(m14, "microseconds")
fig
vec1 <- seq(1000)
vec2 <- seq(1000)
# Define functions to be benchmarked
expand_grid_func <- function() {
return(expand_grid(vec1, vec2))
}
CJ_func <- function() {
return(CJ(vec1, vec2))
}
# Perform benchmarking
m15 <- microbenchmark(
expand_grid = expand_grid_func(),
CJ = CJ_func(),
times = 10
)
print(m15)
## Unit: microseconds
## expr min lq mean median uq max neval
## expand_grid 2185.580 2193.684 2431.7829 2291.2515 2348.754 3447.254 10
## CJ 364.982 372.565 597.7959 431.6355 532.104 1786.695 10
fig <- fbox_plot(m15, "microseconds")
fig
# Sample data
size = 1e4
set.seed(44)
df_list <- replicate(50, data.table(id = sample(seq(size), size, replace = T),
value = rnorm(size)), simplify = F)
simple_bind <- function(list_of_dfs){
do.call(rbind, list_of_dfs)
}
dplyr_bind <- function(list_of_dfs){
bind_rows(list_of_dfs)
}
dt_bind <- function(list_of_dfs){
rbindlist(list_of_dfs, fill = F)
}
# Benchmark both methods
m16 <- microbenchmark(
dt_ver = dt_bind(df_list),
simple = simple_bind(df_list),
dplyr_ver = dplyr_bind(df_list),
times = 30
)
print(m16)
## Unit: microseconds
## expr min lq mean median uq max neval
## dt_ver 452.044 502.128 597.9160 536.5765 577.137 1880.661 30
## simple 488.482 536.932 621.2624 561.7235 613.405 1816.060 30
## dplyr_ver 10104.831 10180.302 10707.3023 10318.7155 10517.521 20018.124 30
fig <- fbox_plot(m16, "microseconds")
fig
set.seed(123)
n <- 1e4
df <- data.table(text = paste("word1", "word2", "word3", "word4", "word5", sep = "."), stringsAsFactors = F)
df <- df[rep(1, n), , drop = F]
# Using tidyr::separate
separate_words <- function() {
df |>
separate(text, into = c("w1", "w2", "w3", "w4", "w5"), sep = "\\.", remove = F) |>
select(-c(w1, w2, w4))
}
# Using stringr::word
stringr_words <- function() {
df |>
mutate(
w3 = word(text, 3, sep = fixed(".")),
w5 = word(text, 5, sep = fixed("."))
)
}
datatable_words <- function() {
df |>
_[, c("w3", "w5") := tstrsplit(text, "\\.")[c(3, 5)]]
}
m17 <- microbenchmark(
separate = separate_words(),
stringr = stringr_words(),
dt = datatable_words(),
times = 10
)
print(m17)
## Unit: milliseconds
## expr min lq mean median uq max neval
## separate 76.67471 83.39636 86.27289 87.43375 90.26654 94.07545 10
## stringr 171.25134 174.47261 192.55688 183.20939 191.68623 282.26948 10
## dt 12.64523 12.83104 13.20490 12.86673 13.09131 15.41465 10
fig <- fbox_plot(m17, "miliseconds")
fig
# Sample data
size = 1e4
n_cores = parallelly::availableCores()
set.seed(123)
df_list <- replicate(100, data.table(id = sample(seq(size), size, replace = T),
value = rnorm(size)), simplify = F)
extra_df <- data.table(id = sample(seq(size), size, replace = T),
extra_value = runif(size))
# Sequential join
sequential_join <- function() {
lapply(df_list, function(df) {
merge(df, extra_df, by = "id", allow.cartesian = T)
})
}
# Parallel join using mclapply
parallel_join <- function() {
mclapply(df_list, function(df) {
merge(df, extra_df, by = "id", allow.cartesian = T)
}, mc.cores = n_cores, mc.silent = T, mc.cleanup = T)
}
# Benchmark both methods
m18 <- microbenchmark(
sequential = sequential_join(),
parallel = parallel_join(),
times = 10
)
print(m18)
## Unit: milliseconds
## expr min lq mean median uq max neval
## sequential 272.6641 276.9203 307.4911 290.0377 355.3227 368.1817 10
## parallel 130.8102 134.8705 141.4624 139.9576 146.2125 157.3999 10
fig <- fbox_plot(m18, "miliseconds")
fig